A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Abstract

This study proposes a novel cloud load prediction model and combines hybrid whale optimizer (HWOA) and extreme learning machine (ELM) together for strong nonlinear mapping ability. Accurate cloud load prediction improves the cloud service efficiency and serves as the foundation for network scheme due to traditional linear forecasting models are unable to predict cloud computing resources with nonlinear changes on massive multiplication and cloud computing data complexity, effectively. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. The contributions of this study are as follows. (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance verifies the benchmark testing functions; and (4) three simulation experiments are conducted to test the cloud load forecast effect. The result indicated that the convergence analysis reveals the HWOA optimizer outperforms the prior optimizers. The proposed cloud load prediction model obtains better forecasting results. The mean absolute percentage error and root mean square error of the proposed model are less than 14% and 11, respectively. Accurate cloud load forecasting lays a foundation for effective deployment of cloud computing resources and maximization of economic benefits.

Multi-granularity three-way decision scheduling optimization based on cloud cluster bursty load

“…1. [4]someone built a temporal cloud load prediction model by improving whale optimization algorithm based on the hybrid strategy (HWOA) and combined with extreme learning machine (ELM) for strong nonlinear mapping ability, which has a good understanding and handling of the nonlinear variation of load over time, which gives us a lot of inspiration to deal with the load variation situation. 2.…”